class_docstr: Categorical distribution parametrized by logits.
code: def log_prob(self, inputs, point):
      inputs = tl.LogSoftmax()(self._unflatten_inputs(inputs))
      return jnp.sum(
          # Select the logits specified by point.
          inputs * tl.one_hot(point, self._n_categories),
          # Sum over the parameter dimensions.
          axis=[-a for a in range(1, len(self._shape) + 2)],
      )
  
imports: import gin
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 5 for calling the Categorical obj's underlying member method code with example usage: obj.log_prob(inputs, point) and returns: jnp